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import gradio as gr
import torch
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
import numpy as np
def get_text(text, hps):
text_norm = text_to_sequence(text, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
hps = utils.get_hparams_from_file("./configs/vtubers.json")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
_ = net_g.eval()
_ = utils.load_checkpoint("./nene_final.pth", net_g, None)
all_emotions = np.load("all_emotions.npy")
emotion_dict = {
"小声": 2077,
"激动": 111,
"平静1": 434,
"平静2": 3554
}
import random
def tts(txt, emotion):
stn_tst = get_text(txt, hps)
randsample = None
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
sid = torch.LongTensor([0])
if type(emotion) ==int:
emo = torch.FloatTensor(all_emotions[emotion]).unsqueeze(0)
elif emotion == "random":
emo = torch.randn([1,1024])
elif emotion == "random_sample":
randint = random.randint(0, all_emotions.shape[0])
emo = torch.FloatTensor(all_emotions[randint]).unsqueeze(0)
randsample = randint
elif emotion.endswith("wav"):
import emotion_extract
emo = torch.FloatTensor(emotion_extract.extract_wav(emotion))
else:
emo = torch.FloatTensor(all_emotions[emotion_dict[emotion]]).unsqueeze(0)
audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1, emo=emo)[0][0,0].data.float().numpy()
return audio, randsample
def tts1(text, emotion):
if len(text) > 150:
return "Error: Text is too long", None
audio, _ = tts(text, emotion)
return "Success", (hps.data.sampling_rate, audio)
def tts2(text):
if len(text) > 150:
return "Error: Text is too long", None
audio, randsample = tts(text, "random_sample")
return str(randsample), (hps.data.sampling_rate, audio)
def tts3(text, sample):
if len(text) > 150:
return "Error: Text is too long", None
try:
audio, _ = tts(text, int(sample))
return "Success", (hps.data.sampling_rate, audio)
except:
return "输入参数不为整数或其他错误", None
app = gr.Blocks()
with app:
with gr.Tabs():
with gr.TabItem("使用预制情感合成"):
tts_input1 = gr.TextArea(label="日语文本", value="こんにちは。私わあやちねねです。")
tts_input2 = gr.Dropdown(label="情感", choices=list(emotion_dict.keys()), value="平静1")
tts_submit = gr.Button("合成音频", variant="primary")
tts_output1 = gr.Textbox(label="Message")
tts_output2 = gr.Audio(label="Output")
tts_submit.click(tts1, [tts_input1, tts_input2], [tts_output1, tts_output2])
with gr.TabItem("随机抽取训练集样本作为情感参数"):
tts_input1 = gr.TextArea(label="日语文本", value="こんにちは。私わあやちねねです。")
tts_submit = gr.Button("合成音频", variant="primary")
tts_output1 = gr.Textbox(label="随机样本id（可用于第三个tab中合成）")
tts_output2 = gr.Audio(label="Output")
tts_submit.click(tts2, [tts_input1], [tts_output1, tts_output2])
with gr.TabItem("使用情感样本id作为情感参数"):
tts_input1 = gr.TextArea(label="日语文本", value="こんにちは。私わあやちねねです。")
tts_input2 = gr.Number(label="情感样本id", value=2004)
tts_submit = gr.Button("合成音频", variant="primary")
tts_output1 = gr.Textbox(label="Message")
tts_output2 = gr.Audio(label="Output")
tts_submit.click(tts3, [tts_input1, tts_input2], [tts_output1, tts_output2])
with gr.TabItem("使用参考音频作为情感参数"):
tts_input1 = gr.TextArea(label="text", value="暂未实现")
app.launch()
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